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From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response
Open accessAbstract
SUMMARY Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.
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“The first iteration, simZFish1.0, successfully replicates key behavioral aspects of zebrafish OMR, partly due to its tuning to match behaviors for limited orthogonal stimuli (25).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Moreover, medial/forward motion-responsive neurons are the most frequent monocular response type in real zebrafish (iMM (25), cf.,’MoNL’ (32)), which respond to medial (nasalward) and forward (translational) motion.”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Current models of zebrafish visuomotor transformations rely on correlation-based neural activity, lack realistic representations of the retina or motor systems (25, 29), omit the ability to manipulate neural connectivity, and exclude closed-loop observations of neural activation during behavior, which limits the integrative investigation of the system within its sensory and physical environment.”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Although it is plausible that alternative neural circuits favoring lower anterior DSGCs connectivity could be equally effective, they would require downstream visuomotor circuits different from those experimentally observed (25).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Specifically, our simulations explain why pretectal neurons in real zebrafish preferentially respond to motion in the lower posterior visual field (25, 30, 31), because with laterally positioned eyes, translational, bottom-projected visual stimuli generate opposing rotational optic flow patterns (Fig. 4E).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“Restricting retinal-pretectal connectivity to only one quadrant, may confer computational advantages, allowing combined processing of forward/medial and backward/lateral motion which drive or reduce locomotion, respectively (25).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Although brain scale imaging (25, 60, 68) and causal manipulations (69) via optogenetics are possible in zebrafish, they are costly and technically challenging, particularly during movement (70).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“Benefitting from detailed experimental data (25, 29–34) and recent advances in physics simulation technologies (35), our open-source simZFish faithfully reproduces the zebrafish OMR, utilizing experimentally observed neural activity (25).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“As suggested by our previous modeling, lPTs collectively control the movement direction by activating neurons of the anterior hindbrain (aHB) (25, 30), which suppresses behavioral oscillation via reciprocal inhibition (Fig. 2E).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“These neurons exist in real zebrafish in at least eight mirror symmetric sets of direction-selective functional response types (25).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“To achieve a biorealistic artificial neural architecture, we synthesized anatomical (30, 41), functional (25, 31, 32), and computational findings (25), creating an embedded multi-layer network model of the OMR visuomotor pathways consisting of rate-coding artificial neurons (Fig. 2A).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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“By initializing simZFish’s circuits with our experimentally derived, best-fit (25) connection weights (Fig. S3G), minimal additional tuning achieved functional connectivity that demonstrated similar activation patterns to zebrafish neurons to the direction and eye-specific stimuli (Fig. 2F, G).”
From Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors (2025) · cites this paper as [25]
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Citations
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Self-citationDissecting a circuit for olfactory behaviour in Caenorhabditis elegans
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Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671
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Ye, L., Bae, M., Cassilly, C. D., Jabba, S. V., Thorpe, D. W., Martin, A. M., Lu, H.-Y., Wang, J., Thompson, J., Lickwar, C. R., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.11.011 · Cited by 338
A Cellular-Resolution Atlas of the Larval Zebrafish Brain
Kunst, M., Laurell, E., Mokayes, N., Kramer, A., Kubo, F., Fernandes, A. M., Förster, D., Maschio, M. D., & Baier, H. (2018) · Neuron · doi:10.1016/j.neuron.2019.04.034 · Cited by 287
Metadata
- Authors
- Naumann, E. A., Fitzgerald, J. E., Dunn, T. W., Rihel, J., Sompolinsky, H., & Engert, F.
- Affiliations
- Harvard University (US)
- University College London (GB)
- Corresponding author
- Engert, Florian
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 167
- Issue
- 4
- Pages
- 947-960.e20
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5111816
- OpenAlex
- W2547071550
- Semantic Scholar
- 85fc104dab138ca2a31f934dc1146b54f5c3cb3a
- Cited by
- 315
- References
- 54
- Citations by year
- 2026: 46 · 2025: 40 · 2024: 22 · 2023: 17 · 2022: 44 · 2021: 40 · 2020: 42 · 2019: 24 · 2018: 25 · 2017: 14
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- NIH (U01NS090449, DP1 NS082121, R24NS086601)
- Simons Foundation (SCGB#325207)
- European Research Council (282027 ZFISHSLEEP)
- Marie Curie Fellowship (PIIF-GA-2012-328847)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Zebrafish Biomedical Research Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- optomotor response, zebrafish, sensorimotor circuits, visual motion processing, interhemispheric inhibition, functional connectivity, locomotor behavior, orienting
- MeSH terms
- Animals, Brain, Neural Pathways, Neurons, Swimming, Visual Perception, Zebrafish, Feedback, Sensory, Neuroimaging
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